Instructions to use octava/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octava/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="octava/image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("octava/image_classification") model = AutoModelForImageClassification.from_pretrained("octava/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from octava/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/octava/image_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://octava/image_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/octava/image_classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 0474998786de4db216448dc135fdb7ded5123fd1d5479c4925bd47dcaa817e95
- Size of remote file:
- 343 MB
- SHA256:
- 7a4d06bc0424b846ab2a55f9d50e1bf2e4f92ccd6c87c4433d10e162ef7789dc
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